supertrain This project implements machine learning models to analyze and predict heart disease using various patient health metrics. The analysis includes Decision Tree , along with comprehensive data visualization using Python and Power BI. Heart Disease Analysis Project

Overview This project implements machine learning models to analyze and predict heart disease using various patient health metrics. The analysis includes both Decision Tree classification for prediction and Kmeans clustering for pattern discovery, along with comprehensive data visualization using Python and Power BI.

Table of Contents Dataset Description Features Installation Project Structure Usage Visualizations Results Power BI Dashboard Contributing

Dataset Description The dataset contains various medical and demographic features of patients, including: Age Gender Blood Pressure Cholesterol Levels Heart Rate And other relevant medical parameters

The target variable indicates the presence (1) or absence (0) of heart disease.

Features Decision Tree Classification Predicts heart disease presence Includes feature importance analysis Visualization of the decision tree structure

Power BI Dashboard Interactive visualizations Key performance indicators Demographic analysis Risk factor correlation

Installation

 Clone the repository
git clone https://github.com/yourusername/heartdiseaseanalysis.git

 Navigate to the project directory
cd heartdiseaseanalysis

 Install required packages
pip install r requirements.txt

Required Libraries: pandas numpy scikitlearn matplotlib seaborn

Power BI Dashboard

1. Open the .pbix file in Power BI Desktop
2. Connect to your data source
3. Refresh the data if needed
4. Interact with the visualizations

Visualizations The project includes various visualizations: Decision Tree structure Feature importance plots Cluster analysis plots Interactive Power BI dashboards Disease distribution Age and gender analysis Risk factor correlations Trend analysis

Results Decision Tree Classification achieves X% accuracy Identified key features for heart disease prediction Discovered distinct patient clusters through Kmeans Created interactive Power BI dashboard for stakeholder analysis

Power BI Dashboard The Power BI dashboard includes: Disease distribution overview Demographic analysis Risk factor analysis Trend visualization Interactive filters and slicers

Key Metrics Displayed: Total patient count Disease prevalence rate Age distribution Gender distribution Risk factor correlations

Contributing

1. Fork the repository
2. Create your feature branch (git checkout b feature/AmazingFeature)
3. Commit your changes (git commit m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License This project is licensed under the MIT License see the LICENSE file for details.

Contact [email protected]